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One of the things that management seems to love the most about a database full of bugs is the reports they can generate to represent the variety of information available in the system. No magic formula or query tells whether your project is ready to ship or if it is in trouble, but there are countless methods of examining the data. Teams at Microsoft examine bug data in hundreds of different permutations. Some examples of potential bug metrics to examine along with potential uses are listed in Table 9-5. Table 9-5: Metrics and Purpose Open table as spreadsheet Metric Metric use
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Total fixed found The ratio of bugs that are fixed versus other resolutions. Early in the product cycle, / total bugs fixed expect to find more bugs than the number that are resolved; later in the ship cycle, expect bugs to be resolved faster than they are found. This metric can also tell you how to build a prediction model of when you will hit zero bugs. Total bugs per language Bug find rate over time View of cost involved in testing a localized version. This metric can provide clues to a more effective localization effort. Too high or too low can be concerning; spikes should be explained.
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Bug fix rate over Percentage of bugs found that are being fixed. Percentage should go down toward time ship as the triage bar rises. Bugs by code area Bugs found by functional area Sorted list of functions with the most reported bugs can influence where additional testing might be needed. Percentage of bugs found by test team, by internal users, by development, by product support, and by external beta testers can help influence test strategy.
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Bugs by severity Expect to see severity 1 and severity 2 bug find rates drop as the project progresses, while percentage of severity 3 and lower bugs increases. That is, expect to find the serious bugs earlier in the product cycle. Where found This measure can vary depending on the type of product being tested. Understanding where in the product the bugs have been found can reveal risky areas of the product. Knowing how the bug was found can aid in root cause analysis and implementation of defect prevention techniques.
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When introduced Knowing at what stage of product development the problem was introduced (for example, specification, design, coding, bug fixing) can influence where defect prevention techniques need to be implemented. Bug reactivation Can be a good indicator of the quality of fixes that Dev provides; this often increases rate toward the end of the project when the bug fix rate is at its highest. Bugs opened by Understand which types of testing are finding bugs. Activities might include testing activity exploratory testing, structured testing, prerelease testing, test case development, configuration testing, printer testing, automated testing, general product use, beta tests, each test pass, acceptance testing, and so forth. Average time to Track development team's level of responsiveness to posted bugs. resolve Average time to Track general responsiveness to bugs, or the time it takes a bug to progress through close the bug workflow. Ideally, bugs are quickly fixed by development, and the bug fix is verified by test promptly. Certainly, as with most metrics, representing bug data as a graph can be advantageous. Figure 9-3 and Figure 9-4 are examples of bug trend information represented in a simple graph format. At Microsoft, these sorts of charts are sent within product teams on a regular basis as a method of quickly communicating relevant information related to bugs.
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Figure 9-3: Active and resolved bugs with projected trend line.
Figure 9-4: Bug find, resolve, and close rate by week.
How Not to Use the Data: Bugs as Performance Metrics
It is tempting to use bug data to measure tester performance. Testers are expected to find bugs, so you might expect good testers to find higher volumes of bugs. Many managers collect and track bug data for performance management. However, alone the gross number of bugs reported metric provides very little valuable information with regard to individual performance. There are simply too many variables in bug counts, especially when comparing between peers, such as the following: Feature complexity Developer ability Specification completeness Bug prevention versus bug finding Timeliness of reporting Additionally, if anyone intends to use specific bug counts as a performance metric, that person must qualify the parameters of the measurement and be prepared to address questions, such as follows: Does the number of bugs reported have to be of a particular severity/priority If yes, what is the breakdown Do functional bugs count equally as superficial user interface type bugs do Will spending time (one or more days) tracking down a critical issue (such as data loss, memory leak) that gets resolved result in not meeting expectations or indicate poor performance If yes, what is the team policy for collaborating with developers to assist in troubleshooting these issues Is bug quality a factor If yes, how are the specific factors for bug quality determined in the group, and what are the team averages Are the averages the target goal What specific quality factors would exceed expectations for the goal What is the minimum number of bugs per measured iteration What is the number of bugs a tester needs to produce to exceed expectations Finding a high number of bugs can indicate that the tester is doing a good job, or it might mean that the developer is doing a poor job of writing code. Conversely, if a tester is finding a low number of bugs, it could be a sign that he is not performing well, or it might mean that he is testing high-quality code that
has a lower bug density. It is crucial to use the metrics at the individual level only to show where additional investigation might be needed. For example, if a tester is not reporting very many bugs, it would make sense to look at the feature area and determine the cause of the low bug count. If other users (customers, developers, beta users) find bugs in the area, the tester's low bug count might be a concern. If there were a low number of tests run (measured by test cases or code coverage information), the low number of bugs might also be something worth investigating. However, if upon further investigation you determine that the area is well tested and just doesn't have very many bugs, it certainly isn't a situation where you would want to penalize the tester. A Story of Bug Metrics When I first started at Microsoft, I had a bug quota. My manager told me that every tester on the team was expected to find 10 bugs per week. That seemed like a reasonable request, so I diligently went to work and started finding bugs. Like most Microsoft employees, I always wanted to do a little more than was expected of me, so I regularly reported at least 12 or 13 bugs per week. Fortunately, the area I was testing was undergoing a lot of change, and I never had a problem reaching the quota. In fact, some weeks I would find 20 or more bugs! When this happened, however, I worried that I had somehow found too many bugs and that I wouldn't be able to reach my quota the next week. So, I reported 13 or so, and "saved" the remaining bugs for the following week just in case my well of bugs dried up. This demonstrates another classic case of getting what you measure. My manager wanted 10 bugs per week, so that's what I gave him regardless of whether I found more bugs. I repeatedly see attempts at using bug metrics to measure individual performance, but these metrics are rarely, if ever, effective.
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